Histomics as an emerging avenue for oncology biomarker development: Computational Histology Artificial Intelligence (CHAI) analysis of The Cancer Genome Atlas (TCGA).

e15008 Background: Biomarkers are critical to precision oncology. The emerging modality of “histomics” is predicated upon the analysis of quantitative histologic features from routine hematoxylin a cut point dichotomized each case into biomarker (+) or (-), with (+) indicating higher risk. The models then the 20 cancer types were grouped into 9 disease sites to simplify reporting. Cox multivariable analysis (MVA) was used to assess associations between the CHAI biomarkers & clinical outcomes. Results: 6117 nonmetastatic TCGA cases had available H&E WSI: 1795 dev and 4322 val. CHAI processed >7000 H&E WSIs, classifying ~2 billion cells and >200 billion μm2 tissue to calculate biomarker scores. A median of 35% of cases were biomarker (+). CHAI biomarker (+) remained significantly associated with worse survival for each disease site after controlling for available clinicopathologic variables including age, sex, and stage with hazard ratios ranging from 1.66-3.25 (Table). Conclusions: The CHAI histomics platform quantified histologic features of prognostic value across cancer types (p≤0.02) from TCGA via cancer-specific histologic signatures. These findings support histomics as a modality for oncology biomarker development in solid tumors. CHAI validation results by disease site in TCGA. Biomarker(+): higher risk. Disease site TCGA cancer type N Biomarker(+) (%) MVA HR 95% CI p GI Luminal COAD, READ, STAD, ESCA 695 262 (38) 1.66 1.22, 2.26 <0.01 Hepatobiliary PAAD, LIHC, CHOL 399 140 (35) 1.72 1.29, 2.30 <0.01 GU PRAD, BLCA, KIRP, KIRC, KICH 989 272 (27) 2.00 1.45, 2.75 <0.01 Lung LUAD, LUSC 621 230 (37) 1.73 1.31, 2.29 <0.01 GYN CESC, OV 235 49 (17) 2.57 1.43, 4.62 <0.01 Endocrine THCA 317 64 (20) 2.79 1.40, 5.57 <0.01 Breast BRCA 643 59 (9) 2.42 1.19, 4.90 0.01 H&N HNSC 140 52 (37) 3.25 1.52, 6.94 0.02 Skin SKCM 280 114 (40) 2.25 1.56, 3.24 <0.01